Novel Adversarial Unsupervised Subdomain Adaption Multi-Channel Deep Convolutional Network for Cross-Operating Fault Diagnosis of Rolling Bearings

Novel Adversarial Unsupervised Subdomain Adaption Multi-Channel Deep Convolutional Network for Cross-Operating Fault Diagnosis of Rolling Bearings

Rolling bearings in production practice usually serve in a healthy state. Some fault state labels are scarce or even no labels, resulting in unbalanced data categories. Meanwhile, frequent working condition switching results in significant differences in data distribution among working conditions, a...

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Bibliographic Details
Main Authors: Bo Zhang, Tianlong Huo, Zheng Liu, Baoquan Hu, Heyue Huang, Zehai Ren, Jianbo Ji
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Intelligent cross-domain fault diagnosis
unbalanced data
adversarial domain adaptation
subdomain adaptation
Online Access:https://ieeexplore.ieee.org/document/10473058/

Internet

https://ieeexplore.ieee.org/document/10473058/

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